Multiobjective Genetic Algorithm Approach to the Economic Statistical Design of Control Charts with an Application to math formulabar and S2 Charts

Authors


Correspondence to: Erwin Saniga, Department of Business Administration Department, University of Delaware, Newark, DE 19716, USA.

E-mail: sanigae@lerner.udel.edu

Abstract

Control charts are the primary tools of statistical process control. These charts may be designed by using a simple rule suggested by Shewhart, a statistical criterion, an economic criterion, or a joint economic statistical criterion. Each method has its strengths and weaknesses. One weakness of the methods of design listed is their lack of flexibility and adaptability, a primary objective of practical mathematical models. In this article, we explore multiobjective models as an alternative for the methods listed. These provide a set of optimal solutions rather than a single optimal solution and thus allow the user to tailor their solution to the temporal imperative of a specific industrial situation. We present a solution to a well-known industrial problem and compare optimal multiobjective designs with economic designs, statistical designs, economic statistical designs, and heuristic designs. Copyright © 2012 John Wiley & Sons, Ltd.

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